Estimating Allocative and Technical Efficiency of Corporate Farms

2005 ◽  
pp. 97-108
Author(s):  
M. Grazdaninova ◽  
Z. Lerman
Keyword(s):  
Survey Data ◽  
Technical Efficiency ◽  
Best Practice ◽  
Allocative Efficiency ◽  
Market Economies ◽  
Input And Output ◽  
Current Input ◽  
Corporate Farms ◽  
Production Technologies ◽  
Productivity Gap

This study is aimed to analyze Russian corporate farms technical efficiency using DEA and allocative efficiency basing upon VMP calculations. The estimations are carried out on three aggregation levels: total farm, sectors (livestock and crops production) and selected commodities (grain, sunflower, beef, milk and pork), using survey data. The results of the analysis suggest that there is no substantial misallocation of resources given current input and output prices. High technical efficiency scores speak for production technologies being homogeneous. Neither "best practice" extension, nor getting to allocative efficiency will eliminate the large productivity gap between Russia and the developed market economies.

scholarly journals ANALYSIS OF TECHNICAL, ALLOCATIVE AND ECONOMIC EFFICIENCIES OF YAM PRODUCERS IN GANYE LOCAL GOVERNMENT AREA OF ADAMAWA STATE, NIGERIA

2020 ◽  
Vol 6 (7) ◽  
pp. 129-143
Author(s):  
A. Aliyu ◽  
A. B Shelleng
Keyword(s):  
Local Government ◽  
Technical Efficiency ◽  
Economic Efficiency ◽  
Best Practice ◽  
Allocative Efficiency ◽  
Local Government Area ◽  
Efficiency Gain ◽  
Average Output ◽  
Absolute Efficiency ◽  
The Mean

The study investigated the technical, Allocative and economics efficiencies of yam producers in Ganye Local government Area, Adamawa State, Nigeria. Combinations of purposive and random sampling techniques were employed using 100 famers from five different wards of the local government. In the first place, five wards were selected and used for this study and twenty farmers were selected from each ward, making a total of 100 famers, twenty. The analytical tool used to achieve the objectives of this study was Data Envelopment Analysis. The results of the study revealed that 57% of the farmers had technical efficiency of 0.81 and above while 43% of the farmers operate at less than 0.81 efficiency level. The mean technical efficiency for the 100 sampled farmers in the study area was 0.78. The farmer with the best practice has a technical efficiency of 1.00 while 0.37 is for the least efficient farmers. This implies that on the average, output fall by 0% from the maximum possible level of 1.00 due to technical inefficiency. The mean allocative efficiency was 0.98. The result indicates that average yam farmer in the state would enjoy cost saving of about 5% while allocative inefficient farmer will have an efficiency gain of 95% to attain the level of most efficient farmer among the respondents. The mean economic efficiency was 0.77. The farmer with the best practice has an economic efficiency of 1.0 while 0.08 was for the least efficient farmers. This implies that on the average, output fall by 52% from the maximum possible level due to inefficiency. Finally, among the constraints identified in the study area, the majority of the respondent attested to the fact that high cost of inputs, transportation problem, lack of credit facilities and storage/preservation problem were the major constraints they faced in yam production in the area. The study concludes that yam farmers in the study area have achieved absolute efficiency in the use of variable inputs. It was found that yam production in the study area is profitable.

scholarly journals Measuring the Effect of Size on Technical Efficiency of the United Arab Emirates Hospitals

2016 ◽  
Vol 9 (3) ◽  
pp. 116 ◽  
Author(s):  
Ashraf Mahate ◽  
Samer Hamidi ◽  
Fevzi Akinci
Keyword(s):  
Technical Efficiency ◽  
United Arab Emirates ◽  
Health Authority ◽  
Allocative Efficiency ◽  
Economies Of Scope ◽  
Optimal Size ◽  
Data Envelopment ◽  
Future Health ◽  
Hospital Size ◽  
Technical And Allocative Efficiency

<p><strong>OBJECTIVE:</strong> The main purpose of this study is to estimate the technical efficiency of the United Arab Emirates (UAE) hospitals and examine the effect of hospital size on estimated technical efficiency scores.</p><p><strong>METHODS: </strong>Using 2012 data from Ministry of Health, Dubai Health Authority, and Health Authority in Abu Dhabi,<strong> </strong>we employed a nonparametric method, data envelopment analysis (DEA), to estimate the technical efficiency of 96 private and governmental hospitals in the UAE. Efficiency scores are calculated using both Banker, Charnes, and Cooper (BCC) and Charnes, Cooper, and Rhodes (CCR) models. </p><p><strong>RESULTS: </strong>The average technical efficiency of the UAE hospitals is estimated at 59% based on the BBC model and at 48% based on the CCR model. The optimal size of a hospital in the UAE is between 100 to 300 beds. We also found evidence of economies of scope between the provision of outpatient and inpatient care in the UAE hospitals.</p><p><strong>CONCLUSION: </strong>Our findings indicate that only one third of the UAE hospitals are technically efficient. There is evidence to suggest that there are considerable efficiency gains yet to be made by many UAE hospitals. Additional empirical research is needed to inform future health policies aimed at improving both the technical and allocative efficiency of hospital services in the UAE. </p>

Productivity growth of Indonesian rice production: sources and efforts to improve performance

2018 ◽  
Vol 67 (9) ◽  
pp. 1792-1815 ◽  
Author(s):  
Joko Mariyono
Keyword(s):  
Technological Change ◽  
Technical Efficiency ◽  
Production Technology ◽  
Productivity Growth ◽  
Scale Effects ◽  
Allocative Efficiency ◽  
Rice Production ◽  
Content Type ◽  
Tfp Growth ◽  
Efficiency Effects

PurposeThe purpose of this paper is to investigate the productivity of rice production by decomposing the growth of total factor productivity (TFP) into four components: technological change, scale effects, technical and allocative efficiencies.Design/methodology/approachThis study employed an econometric approach to decompose TFP growth into four components: technological change, technical efficiency, allocative efficiency and scale effect. Unbalanced panel data used in this study were surveyed in 1994, 2004 and 2014 from 360 rice farming operations. The model used the stochastic frontier transcendental logarithm production technology to estimate the technology parameters.FindingsThe results indicate that the primary sources of TFP growth were technological change and allocative efficiency effects. The contribution of technical efficiency was low because it grew sluggishly.Research limitations/implicationsThis study has several shortcomings, such as very lowR2and the insignificant elasticity of labour presented in the findings. Another limitation is the limited time period panel covering long interval, which resulted in unbalanced data.Practical implicationsThe government should improve productivity growth by allocating more areas for rice production, which enhances the scale and efficiency effects and adjusting the use of capital and material inputs. Extension services should be strengthened to provide farmers with training on improved agronomic technologies. This action will enhance technical efficiency performance and lead to technological progress.Social implicationsAs Indonesian population is still growing at a significant rate and the fact that rice is the primary staple food for Indonesian people, the productivity of rice production should increase continually to ensure social security at a national level.Originality/valueThe productivity growth is decomposed into four components using the transcendental logarithm production technology based on farm-level data. The measure has not been conducted previously in Indonesia, even in rice-producing countries.

scholarly journals ESTIMASI FUNGSI PRODUKSI DAN EFISIENSI ALOKASI SUMBERDAYA PETANI KEDELAI DI LOMBOK BARAT

2006 ◽  
Vol 3 (1) ◽  
pp. 145-148 ◽  
Author(s):  
Nurhayatin Nufus
Keyword(s):  
Technical Efficiency ◽  
Production Function ◽  
Stochastic Frontier ◽  
Allocative Efficiency ◽  
Farm Management ◽  
Management Level ◽  
Stochastic Frontier Production Function ◽  
Stochastic Production ◽  
Frontier Production Function ◽  
Land Size

This research  aims  to analyses  factors  influence  on production  and  resources  allocation  of soybeans  by farmer  at  West Lombok.  Production  function  was estimated  from survey data and technical  efficiency  was used to indicate  farm management  level  through maximum  likelihood,  which  was transformed  into frontier stochastic  production  function.  The land  size,  fertilizer  (urea and  TSP), labor  and pesticide  influence  the production  of soybean  at site.  The technical efficciency  level of Soybean fann was 95,6 percent   The  usage of TSP and pesticide reached allocative efficiency while urea and seeds were al/ocative efficiency yet Key words:  technical  effICiency, allocative  effICiency, and stochastic  frontier  production  function.

scholarly journals Technical Efficiency and Small Scale Maize Producers in Mwanza Region: A Stochastic Frontier Analysis

2021 ◽  
Vol 9 (3) ◽  
Author(s):  
Mukole Kongolo
Keyword(s):  
Technical Efficiency ◽  
Best Practice ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Farm Size ◽  
Small Scale ◽  
Maize Production ◽  
Frontier Analysis

This study measured technical efficiency and its determinants in maize production by small-scale producers in Mwanza region, using a stochastic frontier production function approach. A randomly selected sample of participants in the two districts was used. The Maximum Likelihood estimation procedure was followed to obtain the determinants of technical efficiency and technical efficiency levels of small-scale maize producers. The minimum and maximum values of technical efficiency were between 20% and 91%, indicating that the least practices of specific producer operates at a minimum level of 20%, while the best practice producers  operate  at 91% technical efficiency  level respectively. The summary results of the mean technical efficiency was 63%. The main determinants of technical efficiency were labour, farm size, producer’s experience, producer’s age, family size which were all positive and statistically significant. The findings suggest that the average efficiency of small-scale maize producers could be improved by 37% through better use of existing resources and technology. These findings highlight the need for action by government to assist small-scale maize producers improve efficiency.

scholarly journals Performance of Village Unit Co-Operatives in Yogyakarta Special Region: A Data Envelopment Analysis Approach

2017 ◽  
Vol 1 (2) ◽  
pp. 067
Author(s):  
Abi Pratiwa Siregar ◽  
Jamhari Jamhari ◽  
Lestari Rahayu Waluyati
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Best Practice ◽  
Negative Impact ◽  
Positive Impact ◽  
Three Dimensions ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Rice Milling ◽  
Special Region

This study assessed the performance of 32 village unit co-operatives (KUD) in Yogyakarta Special Region during 2011 to 2012. The efficiency level of the KUD were evaluated by employing the data envelopment analysis and multiple regression analysis using panel data to determine the factors affecting efficiency level. Efficiency analysis was decomposed into three dimensions to explore possible sources of inefficiency. According to Marwa and Aziakpono (2016), the first dimension was technical efficiency, which explored the overall effectiveness of transforming the productive inputs into desired outputs compared to the data-driven frontier of best practice. The second dimension was pure technical efficiency, which captured managerial efficiency in the intermediation process. The third dimension was scale efficiency, which explored whether KUD were operating in an optimal scale of operation or not. The results found that the average scores are 64%, 92%, and 68% for technical, pure technical, and scale efficiency respectively in 2011, while in 2012 the average scores are 57%, 94%, and 60% for technical, pure technical, and scale efficiency. Factors having significantly positive impact on several measures of efficiency are incentive and dummy variables (agriculture inputs and hand tractor). Accounts receivable only has positive relationship to pure technical efficiency. On the other hand, rice milling unit and electricity services have negative impact with several measures of efficiency.

scholarly journals Evaluating Efficiency of Waqf Institutions: An Intermediation Approach Using Data Envelopment Analysis (DEA)

2019 ◽  
Vol 2 (2) ◽  
pp. 82-89
Author(s):  
Nor Tasik Misbahrudin
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Wealth Distribution ◽  
Scale Efficiency ◽  
Optimum Level ◽  
Data Envelopment ◽  
Input And Output ◽  
Distribution State ◽  
Using Data ◽  
Decision Making Units

Waqf is a voluntary charity that cannot be disposed of and the ownership cannot be transferred once it is declared as waqf assets. Waqf institutions play an important role in helping the development of Muslims ummah through wealth distribution. State Islamic Religious Councils (SIRCs) in Malaysia are the sole trustee that manage and develop waqf assets. Based on selected input and output, the intermediary approach assumes that cash waqf received as output while total expenditure of SIRCs as input. Under this approach SIRCs act as intermediary between waqif (giver) and beneficiaries. Thus, this paper attempts to analyze the efficiency of waqf institutions in Malaysia by using Data Envelopment Analysis (DEA) method under output-orientation using Variable Return to Scale (VRS) assumptions. Four SIRCs were selected as decision making units (DMU) for the period of 2011 to 2015. The result indicates that changes in average technical efficiency for every year is contributed by both pure technical and scale. However, inefficiency of Malaysian waqf institutions is mostly contributed by pure technical efficiency aspects rather than scale. 2012 showed the highest average technical efficiency with 73.9% as most of the institutions operated in optimum level of input to produce output. Thus, the result suggests that both technical and scale efficiency should be improved to achieve the most efficient and productive level of performance in order to fulfill objectives of the institutions as an intermediary between waqif and beneficiaries.

Best Practice in Online Survey Research with Sensitive Topics

2013 ◽  
pp. 157-168 ◽  
Author(s):  
Kristina M. Kays ◽  
Tashina L. Keith ◽  
Michael T. Broughal
Keyword(s):  
Gender Differences ◽  
Survey Research ◽  
Survey Data ◽  
Best Practice ◽  
Online Survey ◽  
Survey Design ◽  
Sensitive Material ◽  
Sensitive Topics ◽  
Need To Evaluate

This chapter addresses the main considerations in online survey research with sensitive topics. Advances in technology have allowed numerous options in addressing survey design, and thus created a need to evaluate and consider best approaches when using online survey research. This chapter identifies subjects such as item non-response in online survey research. In addition, this chapter includes a description of the differences in researching non-sensitive topics versus sensitive topics, and then lists a number of best practice strategies to reduce item non-response and improve the quality of survey data obtained. Included are specific considerations for defining sensitive topics and addressing gender differences when surveying more sensitive material. Additional resources in online survey research design are recommended.

Selecting Suitable Probes Distances for Sizing Deep Surface Cracks Using the DCPD Technique

2006 ◽  
Vol 129 (1) ◽  
pp. 205-210 ◽  
Author(s):  
Fumio Takeo ◽  
Masumi Saka ◽  
S. Reaz Ahmed ◽  
Seiichi Hamada ◽  
Manabu Hayakawa
Keyword(s):  
Three Dimensional ◽  
Potential Drop ◽  
Surface Cracks ◽  
Deep Surface ◽  
Input And Output ◽  
Wide Range ◽  
Current Input ◽  
Dimensional Finite Element ◽  
Three Dimensional Finite Element ◽  
The Way

In this study, the way to enhance the sensitivity of evaluating deep surface cracks by DCPD technique using four probes is considered. The potential drops across two-dimensional cracks having different depths are analyzed by the three-dimensional finite-element method. The effect of the distance between current input and output probes and the distance between measuring probes on the change in potential drops are analyzed for a wide range of crack depths. By extending the distance between current input and output probes, the change in potential drop with the change in the depth of deeper crack becomes large. But the voltage of potential drop becomes small to measure. Finally, the way to select the appropriate distances between the probes for the measuring sensor is shown from the viewpoints of sensitivity and the required current.

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